A uniform phase representation for the harmonic model in speech synthesis applications

Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

Surface plasmons and effects of losses in propagating modes in a chain of silver nanoparticles

In this work a chain of 4000 silver nanoparticles embedded in a glass medium is considered, and its leftmost particle is excited by an electric field pulse of Gaussian shape. Considering Drude’s model, losses of the system are taken into account by γ factor, which stands for the Ohmic losses, and different quantities, such as frequencies of excited modes and group velocities are calculated. Besides, these results are compared to those obtained from the dispersion relation of an infinite chain. The increase of losses affects the lifetime and propagation length of the plasmon; besides, although the response dispersion relation for an infinite chain seems to remain invariable, this is not the case for a finite chain. The mismatches are bigger for higher losses. Furthermore, plasmon propagation velocities are analysed, and an explanation for the mismatch of longitudinal modes close to the intersection point with the dispersion of light is suggested. Finally, some concepts to treat this problem from the energy transport point of view are introduced.

Segmentação de nome e endereço por meio de modelos escondidos de Markov e sua aplicação em processos de vinculação de registros

A segmentação dos nomes nas suas partes constitutivas é uma etapa fundamental no processo de integração de bases de dados por meio das técnicas de vinculação de registros. Esta separação dos nomes pode ser realizada de diferentes maneiras. Este estudo teve como objetivo avaliar a utilização do Modelo Escondido de Markov (HMM) na segmentação nomes e endereços de pessoas e a eficiência desta segmentação no processo de vinculação de registros. Foram utilizadas as bases do Sistema de Informações sobre Mortalidade (SIM) e do Subsistema de Informação de Procedimentos de Alta Complexidade (APAC) do estado do Rio de Janeiro no período entre 1999 a 2004. Uma metodologia foi proposta para a segmentação de nome e endereço sendo composta por oito fases, utilizando rotinas implementadas em PL/SQL e a biblioteca JAHMM, implementação na linguagem Java de algoritmos de HMM. Uma amostra aleatória de 100 registros de cada base foi utilizada para verificar a correção do processo de segmentação por meio do modelo HMM.Para verificar o efeito da segmentação do nome por meio do HMM, três processos de vinculação foram aplicados sobre uma amostra das duas bases citadas acima, cada um deles utilizando diferentes estratégias de segmentação, a saber: 1) divisão dos nomes pela primeira parte, última parte e iniciais do nome do meio; 2) divisão do nome em cinco partes; (3) segmentação segundo o HMM. A aplicação do modelo HMM como mecanismo de segmentação obteve boa concordância quando comparado com o observador humano. As diferentes estratégias de segmentação geraram resultados bastante similares na vinculação de registros, tendo a estratégia 1 obtido um desempenho pouco melhor que as demais. Este estudo sugere que a segmentação de nomes brasileiros por meio do modelo escondido de Markov não é mais eficaz do que métodos tradicionais de segmentação.

Avaliação do custo-efetividade da incorporação do Espectômetro de Massa em Tandem no Programa Nacional de Triagem Neonatal no Brasil para diagnóstico da Fenilcetonúria e MCAD.

A Espectrometria de Massa em Tandem (MS/MS) é mundialmente considerada padrão ouro para a Triagem Neonatal (TN) de Erros Inatos do Metabolismo (IEM). Além de apresentar melhor sensibilidade e especificidade possibilita rastrear uma vasta gama de IEM usando um único teste. Atualmente o Programa Nacional de Triagem Neonatal (PNTN) rastreia cinco doenças (Fenilcetonúria, Hipotiroidismo Congênito, Fibrose Cística, Hemoglobinopatias e Deficiência da Biotinidase). Uma das metas do PNTN é o aprimoramento e a incorporação de novas doenças e/ou tecnologias. Com a recente recomendação da CONITEC (Comissão Nacional de Incorporação de Tecnologias) para aquisição do MS/MS para diagnóstico de doenças raras, vislumbra-se o incremento desta tecnologia para ampliação de doenças triadas, melhora da qualidade do teste diagnóstico, corroborando para melhorar qualidade de vida das crianças acometidas pelos EIM. Este trabalho teve como objetivo realizar uma análise de custo efetividade, para incorporação da tecnologia de tandem MS/MS na triagem neonatal, sob a perspectiva do SUS. Desta maneira buscou-se comparar diferentes cenários da TN com a tecnologia atualmente utilizada (Fluorimetria) somente para Fenilcetonúria (PKU), e com MS/MS para rastreio da PKU e da Deficiência de Cadeia Média Acyl-Coenzima Desidrogenase (MCAD). Para tanto construiu-se um modelo matemático de decisão baseados em cadeias de Markov que simulou a TN da PKU e da MCAD, bem como a história natural da MCAD. Foi acompanhada uma coorte hipotética de cem mil recém-nascidos. O horizonte temporal adotado foi a expectativa de vida da população brasileira de 78 anos de acordo com IBGE. Utilizou-se uma taxa de desconto de 5% para os custos e consequências clínicas para ambos os cenários propostos. Quando incorporado o MS/MS para triagem da PKU os ganhos em saúde continuaram os mesmos, pois o desempenho do MS/MS e da Fluorimetria foram praticamente iguais (efetividade), porém o custo incremental foi quatro vezes maior para a mesma efetividade, o que torna o MS/MS somente para PKU não custo efetiva (dominada). No entanto, quando analisado o cenário do MS/MS para triagem da PKU e da MCAD o custo incremental do MS/MS no PNTN foi menor por causa da economia feita uma vez que é possível realizar ambos os testes no mesmo o teste do pezinho atual.

On the relationship between hidden Markov Models and convex functional transforms for simulating scanning probe Microscopy

Models for simulating Scanning Probe Microscopy (SPM) may serve as a reference point for validating experimental data and practice. Generally, simulations use a microscopic model of the sample-probe interaction based on a first-principles approach, or a geometric model of macroscopic distortions due to the probe geometry. Examples of the latter include use of neural networks, the Legendre Transform, and dilation/erosion transforms from mathematical morphology. Dilation and the Legendre Transform fall within a general family of functional transforms, which distort a function by imposing a convex solution.In earlier work, the authors proposed a generalized approach to modeling SPM using a hidden Markov model, wherein both the sample-probe interaction and probe geometry may be taken into account. We present a discussion of the hidden Markov model and its relationship to these convex functional transforms for simulating and restoring SPM images.©2009 SPIE.

Enhanced supply chain tracking based on the EPC network: A Bayesian approach

Supply chain tracking information is one of the main levers for achieving operational efficiency. RFID technology and the EPC Network can deliver serial-level product information that was never before available. However, these technologies still fail to meet the managers' visibility requirements in full, since they provide information about product location at specific time instances only. This paper proposes a model that uses the data provided by the EPC Network to deliver enhanced tracking information to the final user. Following a Bayesian approach, the model produces realistic ongoing estimates about the current and future location of products across a supply network, taking into account the characteristics of the product behavior and the configuration of the data collection points. These estimates can then be used to optimize operational decisions that depend on product availability at different locations. The enhancement of tracking information quality is highlighted through an example. © 2009 IFAC.

Convex approaches to model wavelet sparsity patterns

Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in de-convolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso. © 2011 IEEE.

Recent advance in the study of ribosomal model.

With the technological developments of cryoelectron microscope, X-ray diffraction and the growing data available on various components of ribosome, some marvelously intricate structural models of the Escherichia coli 70S ribosome have been reconstructed. The picture of the ribosomal model are detailed, including the placement of the mRNA, the arrangement of the A-site and P-site tRNAs and the peptidyltransferase within the interface gap as well as the path of nascent polypeptide chain, which results in a better understanding of the structure and function of ribosome as well as the translational process.

Partially Observable Markov Decision Processes with continuous observations for dialogue management

This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a testbed simulated dialogue management problem, we show how recent optimization techniques are able to find a policy for this continuous POMDP which outperforms a traditional MDP approach. Further, we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring, including confidence score information. Experiments on the testbed system show significant improvements for several example handcrafted dialogue managers across a range of operating conditions.

Physical consequences of a nonparametric uncertainty model in structural dynamics

One of the main claims of the nonparametric model of random uncertainty introduced by Soize (2000) [3] is its ability to account for model uncertainty. The present paper investigates this claim by examining the statistics of natural frequencies, total energy and underlying dispersion equation yielded by the nonparametric approach for two simple systems: a thin plate in bending and a one-dimensional finite periodic massspring chain. Results for the plate show that the average modal density and the underlying dispersion equation of the structure are gradually and systematically altered with increasing uncertainty. The findings for the massspring chain corroborate the findings for the plate and show that the remote coupling of nonadjacent degrees of freedom induced by the approach suppresses the phenomenon of mode localization. This remote coupling also leads to an instantaneous response of all points in the chain when one mass is excited. In the light of these results, it is argued that the nonparametric approach can deal with a certain type of model uncertainty, in this case the presence of unknown terms of higher or lower order in the governing differential equation, but that certain expectations about the system such as the average modal density may conflict with these results. © 2012 Elsevier Ltd.

Intelligent products in the supply chain - 10 years on

Ten years ago the intelligent product model was introduced as a means of motivating a supply chain in which product or orders were central as opposed to the organizations that stored or delivered them. This notion of a physical product influencing its own movement through the supply chain was enabled by the evolution of low cost RFID systems which promised low cost connection between physical goods and networked information environments. In 2002 the notion of product intelligence was regarded as a useful but rather esoteric construct. However, in the intervening ten years there have been a number of technological advances coupled with an increasingly challenged business environment which make the prospects for intelligent product deployment seem more likely. This paper reviews a number of these developments and assesses their impact on the intelligent product approach. © 2012 IFAC.

The incentives for supply chain collaboration to improve material efficiency in the use of steel: An analysis using input output techniques

In the face of increasing demand and limited emission reduction opportunities, the steel industry will have to look beyond its process emissions to bear its share of emission reduction targets. One option is to improve material efficiency - reducing the amount of metal required to meet services. In this context, the purpose of this paper is to explore why opportunities to improve material efficiency through upstream measures such as yield improvement and lightweighting might remain underexploited by industry. Established input-output techniques are applied to the GTAP 7 multi-regional input-output model to quantify the incentives for companies in key steel-using sectors (such as property developers and automotive companies) to seek opportunities to improve material efficiency in their upstream supply chains under different short-run carbon price scenarios. Because of the underlying assumptions, the incentives are interpreted as overestimates. The principal result of the paper is that these generous estimates of the incentives for material efficiency caused by a carbon price are offset by the disincentives to material efficiency caused by labour taxes. Reliance on a carbon price alone to deliver material efficiency would therefore be misguided and additional policy interventions to support material efficiency should be considered. © 2013 Elsevier B.V.

Intelligent products in the supply chain - 10 Years on

Ten years ago the intelligent product model was introduced as a means of motivating a supply chain in which product or orders were central as opposed to the organizations that stored or delivered them. This notion of a physical product influencing its own movement through the supply chain was enabled by the evolution of low cost RFID systems which promised low cost connection between physical goods and networked information environments. In 2002 the notion of product intelligence was regarded as a useful but rather esoteric construct. However, in the intervening ten years there have been a number of technological advances coupled with an increasingly challenged business environment which make the prospects for intelligent product deployment seem more likely. This paper reviews a number of these developments and assesses their impact on the intelligent product approach. © Springer-Verlag Berlin Heidelberg 2013.

Quantum phase transitions in the S=1/2 distorted diamond chain

By means of the second derivative of the ground-state and first-excited energy, the quantum phase transitions (QPTs) for the distorted diamond chain (DDC) with ferromagnetic and antiferromagnetic frustrated interactions and the trimerized case are investigated, respectively. Our results show the plentiful quantum phases owing to the spin interaction competitions in the model. Meanwhile, by using the transfer-matrix renormalization-group technique, we study the two-site thermal entanglement of the DDC model in the thermodynamic limit for a further understanding of the QPTs.

Quantum critical phenomena in the XY spin chain with the Dzyaloshinski-Moriya interaction (Retracted article. See vol 80, artn 019903, 2009)

We study the effects of the Dzyaloshinski-Moriya (DM) anisotropic interaction on the ground-state properties of the Heisenberg XY spin chain by means of the fidelity susceptibility, order parameter, and entanglement entropy. Our results show that the DM interaction could influence the distribution of the regions of quantum phase transitions and cause different critical regions in the XY spin model. Meanwhile, the DM interaction has effective influence on the degree of entanglement of the system and could be used to increase the entanglement of the spin system.